TITLE

Rage Against the Machine? Generative AI Exposure, Subjective Risk, and Policy Preferences: Replication Package


AUTHORS

(corr.) Matthias Haslberger - matthias.haslberger@unisg.ch
Jane Gingrich
Jasmine Bhatia

Please contact the corresponding author with any questions or issues with this replication package. 


DESCRIPTION

This package contains data and code to reproduce the analysis and figures in the manuscript "Rage Against the Machine? Generative AI Exposure, Subjective Risk, and Policy Preferences," published in the Journal of European Public Policy (http://dx.doi.org/10.1080/13501763.2025.2554903).

This manuscript investigates how exposure to generative AI impacts respondent perspectives of risks to the labour market and policy preferences, including analysis of different subgroups (e.g. gender, occupation). 

All code to replicate the analysis is written in R. 4 files in total are used to replicate the analysis in the article: 2 r-scripts and 2 data files.

The scripts use the R package "pacman" to install and load relevant packages, which is handled by the function pacman::p_load(). To make sure the function runs, the replicator should have "pacman" installed.

When running the analysis it is important that 00_helperfunctions.R is loaded into R. This file contains a list of extra functions used throughout the analysis.

All of the analyses are contained in the file 01_main_analysis.R. First, we replicate all the results in the main paper. Then we replicate the appendices included in the supplementary materials.


CONTENTS

data.rds - cleaned replication dataset
textdata.rds - open-ended responses for text analysis
00_helperfunctions.r – helper functions for analysis
01_main_analysis.r - main analysis code to reproduce figures and tables


CITATION

Please use the following citation if you use this data:

Haslberger, M., Gingrich, J. and Bhatia, J. (2025). Rage Against the Machine? Generative AI Exposure, Subjective Risk, and Policy Preferences: Replication Package (v1.0). https://doi.org/10.7910/DVN/YOSGEL.


METHODS

Data was generated by an online survey experiment fielded by YouGov in the United Kingdom in July 2023. Sample includes 1,041 working age respondents with a treatment group (504 respondents) and control group (537 respondents). Data cleaning and analysis were conducted in R. 

Study received ethical approval from Oxford University's Department of Social Policy and Intervention and adheres to APSA's Principles and Guidelines on Human Subjects Research. 


FUNDING

This project has been funded in part by the Canadian Institute for Advanced Research (CIFAR) and the Schweizerisches Staatssekretariat für Bildung, Forschung und Innovation (SERI) in the framework of the GOVPET research project.
